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#ifndef PCL_FEATURES_LINEAR_LEAST_SQUARES_NORMAL_H_
#define PCL_FEATURES_LINEAR_LEAST_SQUARES_NORMAL_H_

#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/features/feature.h>

namespace pcl
{
  /**
    * \brief Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation.
    * \author Stefan Holzer, Cedric Cagniart
    */
  template <typename PointInT, typename PointOutT>
  class LinearLeastSquaresNormalEstimation : public Feature<PointInT, PointOutT>
  {

    using Feature<PointInT, PointOutT>::input_;
    using Feature<PointInT, PointOutT>::feature_name_;
    using Feature<PointInT, PointOutT>::tree_;
    using Feature<PointInT, PointOutT>::k_;

  public:

    typedef typename Feature<PointInT, PointOutT>::PointCloudIn  PointCloudIn;
    typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;

    /** \brief Constructor */
    LinearLeastSquaresNormalEstimation () :
        use_depth_dependent_smoothing_(false),
        max_depth_change_factor_(1.0f),
        normal_smoothing_size_(9.0f)
    {
        feature_name_ = "LinearLeastSquaresNormalEstimation";
        tree_.reset ();
        k_ = 1;
    };

    /** \brief Destructor */
    virtual ~LinearLeastSquaresNormalEstimation ();

    /** \brief Computes the normal at the specified position. 
      * \param pos_x x position (pixel)
      * \param pos_y y position (pixel)
      * \param normal the output estimated normal 
      */
    void
    computePointNormal (const int pos_x, const int pos_y, PointOutT &normal);

    /** \brief Set the normal smoothing size
      * \param normal_smoothing_size factor which influences the size of the area used to smooth normals 
      * (depth dependent if useDepthDependentSmoothing is true)
      */
    void
    setNormalSmoothingSize (float normal_smoothing_size)
    {
      normal_smoothing_size_ = normal_smoothing_size;
    }

    /** \brief Set whether to use depth depending smoothing or not
      * \param use_depth_dependent_smoothing decides whether the smoothing is depth dependent
      */
    void
    setDepthDependentSmoothing (bool use_depth_dependent_smoothing)
    {
      use_depth_dependent_smoothing_ = use_depth_dependent_smoothing;
    }

    /** \brief The depth change threshold for computing object borders
      * \param max_depth_change_factor the depth change threshold for computing object borders based on 
      * depth changes
      */
    void 
    setMaxDepthChangeFactor (float max_depth_change_factor)
    {
      max_depth_change_factor_ = max_depth_change_factor;
    }

    /** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
      * \param cloud the const boost shared pointer to a PointCloud message
      */
    virtual inline void 
    setInputCloud (const typename PointCloudIn::ConstPtr &cloud) 
    { 
      input_ = cloud; 
    }

  protected:

    /** \brief Computes the normal for the complete cloud. 
      * \param output the resultant normals
      */
    void 
    computeFeature (PointCloudOut &output);

  private:

    /** the threshold used to detect depth discontinuities */
    //float distance_threshold_;

    /** \brief Smooth data based on depth (true/false). */
    bool use_depth_dependent_smoothing_;

    /** \brief Threshold for detecting depth discontinuities */
    float max_depth_change_factor_;

    /** \brief */
    float normal_smoothing_size_;

    /** \brief Make the computeFeature (&Eigen::MatrixXf); inaccessible from outside the class
      * \param[out] output the output point cloud 
      */
    void 
    computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &) {}
  };
}

#endif 

